Anthony Alcaraz

Amazon Web Services (AWS)

Driving growth in the startup ecosystem by leveraging AWS's cutting-edge AI/ML technologies. I help startups accelerate their go-to-market strategies and technological development.

Key focus areas:
- Developing AI/ML startups
- Guiding high-potential startups through AWS's AI/ML offerings
- Fostering partnerships within the EMEA tech community
- Aligning AWS's AI/ML solutions with startup needs

Passionate about empowering the next wave of AI-driven innovation. 

Let's connect and explore how AWS can supercharge your startup's AI/ML journey!

Senior AI/ML BD/Strategist Manager EMEA

Fribl

Advisor

GenAI x HR 
Designed AI strategy
Lead engineering team 

With recruiting topping the list of concerns for many CEOs and companies spending upwards of $4,000 on average per hire, the need for innovation is clear. 

All too often, the arduous recruiting process leaves both employers and applicants frustrated after 42 days of effort with uncertainty if the right match was made...

At Fribl , we are leading the charge to transform this status quo, starting with reinventing the screening process using our proprietary GenAI technology enhanced by symbolic AI. 

This powerful combination doesn't just accelerate hiring, but actually personalizes the experience to precisely match candidates with roles that align to the company's unique culture - a monumental undertaking previously thought impossible.

As innovations like video interviews, CV parsers and AI recommendations shake up recruitment, we remain laser focused on four seismic shifts: Diversity, Advanced Interviewing, Data Analytics and AI Adoption. 

Our mission? To disrupt outdated screening tactics by simplifying the complex, multidimensional analysis of applicant credentials at scale to surface the ideal talents that personify an organization's ethos.

Chief AI/ML Product Officer & Partner

University of Oxford

Guest lecturer to Ajit Joakar Course : Artificial Intelligence: Generative AI, Cloud and MLOps (online)
Subject : the intersection of LLMs and graphs

AI/ML Guest Lecturer

Aldecis

My client portfolio was L’Oreal Finance Transformation 
AI for BI 

Management in AI Projects and Engineering Teams:
In my managerial role, I oversee AI projects and lead teams of engineers in crafting cutting-edge solutions. My focus is on driving strategic initiatives, ensuring projects are aligned with organizational goals, and fostering a culture of innovation and excellence. I take a hands-on approach to problem-solving and mentorship, ensuring that both projects and people reach their fullest potential.

Development in LLM/ML Ops:
My development expertise lies in the specialized domains of Large Language Models Operations (LLM Ops) and Machine Learning Operations (ML Ops). I am responsible for streamlining machine learning workflows from development to deployment. My work enables organizations to not only adopt but excel in leveraging machine learning technologies, facilitating continuous learning and adaptability across the board.

Marketing and Strategic Partnerships:
In the realm of marketing, I focus on building and nurturing strategic partnerships. My efforts are geared towards creating synergies with like-minded organizations and industry leaders. By forming valuable partnerships, I amplify brand visibility, foster innovation, and open doors to new business opportunities. The result is a win-win situation for all stakeholders involved, enhancing both market reach and impact.

Chief AI/ML Product Officer

Sécurité sociale

AI for performance management and customer support

I served as a Senior Executive after a competitive exam (EN3S). 
In this multifaceted role, I spearheaded a range of initiatives across key sectors such as Finance, Fraud Detection, Quality Control, Accounting, and Data & Performance Management.

Signature Project in ML Ops:
Facing the pervasive challenge of managing recurring incoming flows, I took the lead in conceptualizing and executing an avant-garde Proof of Concept (POC). This POC employed Azure ML-based ML Ops solutions, setting a precedent by being the organization's inaugural foray into Machine Learning Operations. The result was a transformative improvement in operational efficiency and service delivery.

Influence on Pluriannual Strategy 2023-2027 :
My pioneering project had a profound impact on the organization's multi-year strategic plan, specifically in the following domains:

Predictive Analytics: My efforts catalyzed the adoption of predictive analytics, enabling the organization to preemptively identify and mitigate challenges in benefits delivery. This proactive methodology revolutionized our service approach.

Sentiment Analysis: The launch of the "Lab Parentalité" was a critical milestone. Through the use of sentiment analysis, we gained unprecedented insights into parent feedback. This initiative not only enriched our data but also helped us better comprehend and meet the needs of parents.

Chatbots & Virtual Assistants: I played a key role in overhauling our customer service paradigm. By integrating cutting-edge chatbots and virtual assistants, we were able to enhance customer engagement across multiple platforms, including phone support, online portals, and mobile apps.

Personalization Algorithms: To elevate the user experience, we incorporated algorithms designed to customize services and recommendations. This bespoke approach significantly increased user satisfaction by tailoring the experience to individual preferences and needs.

Writes on ML/LLM Ops 🎨 | Follow me (4,8 K+) 🔔

"But logic is not all, one needs one’s heart to follow an idea.” (Richard P. Feynman) 

I am deeply involved in the AI community, where my expertise in decision science, particularly at the intersection of Large Language Models (LLM), Natural Language Processing (NLP), Knowledge Graphs, and Graph Theory, is applied to foster innovation and strategic development. This specialization has positioned me as a leading voice in the construction of Retrieval-Augmented Generation (RAG) and reasoning engines, regarded by many, including myself, as the state-of-the-art approach in our field.

I am also an avid writer, sharing daily insights on AI applications in business and decision-making with my 30,000+ followers on Medium. My articles aim to demystify the complexities of AI and provide actionable advice for leveraging technology in business contexts. You can follow my writings and engage with the community on Medium and LinkedIn.

This dedication to AI and its potential in transforming industries led me to share my knowledge and insights as a speaker, including a recent lecture at Oxford on the integration of LLMs and graphs for contemporary business practices. 

Engaging in discussions about AI and its multifaceted applications in business and beyond is not just my profession but a true passion. I look forward to connecting with fellow enthusiasts and professionals keen on exploring the vast possibilities that AI offers.


Chief AI Officer & Decision scientist

Management in AI Projects and Engineering Teams:
In my managerial role, I oversee AI projects and lead teams of engineers in crafting cutting-edge solutions. My focus is on driving strategic initiatives, ensuring projects are aligned with organizational goals, and fostering a culture of innovation and excellence. I take a hands-on approach to problem-solving and mentorship, ensuring that both projects and people reach their fullest potential.

Development in LLM/ML Ops:
My development expertise lies in the specialized domains of Large Language Models Operations (LLM Ops) and Machine Learning Operations (ML Ops). I am responsible for streamlining machine learning workflows from development to deployment. My work enables organizations to not only adopt but excel in leveraging machine learning technologies, facilitating continuous learning and adaptability across the board.

Marketing and Strategic Partnerships:
In the realm of marketing, I focus on building and nurturing strategic partnerships. My efforts are geared towards creating synergies with like-minded organizations and industry leaders. By forming valuable partnerships, I amplify brand visibility, foster innovation, and open doors to new business opportunities. The result is a win-win situation for all stakeholders involved, enhancing both market reach and impact.

Graph-Native Intelligence - A Path to Sustainable AI Development 🖇️ 


Agentic graph development move beyond traditional language models and retrieval systems toward truly intelligent, autonomous systems. 

This shift is characterized by the integration of graph-native knowledge structures, sophisticated agency, and hybrid reasoning capabilities, creating systems that can not only process information but actively learn, reason, and adapt to complex environments.

First, the emergence of graph-native architectures marks a decisive break from traditional approaches. Unlike conventional systems that treat knowledge as flat structures or simple embeddings, graph-native systems represent information through rich networks of relationships, enabling sophisticated reasoning and contextual understanding. 

This architectural shift allows systems to maintain complex webs of relationships while actively learning and adapting to individual users' needs and preferences.

Second, the integration of neural-symbolic reasoning capabilities represents a crucial advancement in artificial intelligence. 

By combining the pattern-recognition strengths of neural networks with explicit logical reasoning of symbolic systems, these architectures enable verifiable reasoning chains while maintaining the flexibility to handle novel situations. 

This hybrid approach addresses one of the fundamental challenges in AI: bridging the gap between statistical learning and logical reasoning.

Third, the architecture incorporates sophisticated evaluation frameworks, structured output mechanisms, and reliable tool integration patterns. 
These systems are designed not just for experimental implementations but for deployment in enterprise environments where reliability and performance are paramount.

The graph-native approach to knowledge representation enables systems to maintain context and relationships across multiple interactions, leading to more sophisticated understanding and decision-making capabilities. 
This persistent memory architecture allows systems to build up understanding over time, rather than treating each interaction as isolated events.

The integration of multiple specialized agents, each maintaining its own decision-making loop while sharing a common knowledge structure, enables complex task decomposition and parallel processing. 

This multi-agent architecture, combined with graph-based knowledge representation, creates systems that can handle increasingly sophisticated tasks while maintaining explainability and verifiability.

The focus on structured output and reliability addresses critical challenges in deploying AI systems in production environments. 
By ensuring that outputs conform to expected schemas and formats, these systems can maintain reliability while still leveraging the powerful capabilities of modern language models and reasoning engines.

Senior AI/ML Strategist Startups & VC @AWS - Writing on AI/ML, analysis are my own 👌

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